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ORCIDAndreas Damianou
Person information
- affiliation: Spotify
- affiliation (former): Amazon Research Cambridge, UK
- affiliation (former): University of Sheffield, Department of Computer Science, UK
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2020 – today
- 2024
[c25]Marco De Nadai
, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, David Nyhan, Hugues Bouchard, Mounia Lalmas, Andreas Damianou:
Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks. WWW (Companion Volume) 2024: 403-412- [c24]Andreas Damianou, Francesco Fabbri, Paul Gigioli, Marco De Nadai, Alice Wang, Enrico Palumbo, Mounia Lalmas:
Towards Graph Foundation Models for Personalization. WWW (Companion Volume) 2024: 1798-1802 - [i23]Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, David Nyhan, Hugues Bouchard, Mounia Lalmas-Roelleke, Andreas Damianou:
Personalized Audiobook Recommendations at Spotify Through Graph Neural Networks. CoRR abs/2403.05185 (2024) - [i22]Andreas Damianou, Francesco Fabbri, Paul Gigioli, Marco De Nadai, Alice Wang, Enrico Palumbo, Mounia Lalmas:
Towards Graph Foundation Models for Personalization. CoRR abs/2403.07478 (2024) - [i21]Andrea Bacciu, Enrico Palumbo, Andreas Damianou, Nicola Tonellotto, Fabrizio Silvestri:
Generating Query Recommendations via LLMs. CoRR abs/2405.19749 (2024) - 2023
- [c23]Enrico Palumbo, Andreas Damianou, Alice Wang, Alva Liu, Ghazal Fazelnia, Francesco Fabbri, Rui Ferreira, Fabrizio Silvestri, Hugues Bouchard, Claudia Hauff, Mounia Lalmas, Ben Carterette, Praveen Chandar, David Nyhan:
Graph Learning for Exploratory Query Suggestions in an Instant Search System. CIKM 2023: 4780-4786 - 2021
- [j3]Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek:
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis. J. Mach. Learn. Res. 22: 86:1-86:51 (2021) - [c22]Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno, Andrew Gordon Wilson, Andreas C. Damianou:
Fast Adaptation with Linearized Neural Networks. AISTATS 2021: 2737-2745 - [c21]Francesco Tonolini, Pablo Garcia Moreno, Andreas C. Damianou, Roderick Murray-Smith:
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data. ICLR 2021 - [i20]Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno, Andrew Gordon Wilson, Andreas C. Damianou:
Fast Adaptation with Linearized Neural Networks. CoRR abs/2103.01439 (2021) - 2020
- [c20]Shell Xu Hu, Pablo Garcia Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas C. Damianou:
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients. ICLR 2020 - [i19]Shuai Tang, Wesley J. Maddox, Charlie Dickens, Tom Diethe, Andreas C. Damianou:
Similarity of Neural Networks with Gradients. CoRR abs/2003.11498 (2020) - [i18]Benjamin van Niekerk, Andreas C. Damianou, Benjamin Rosman:
Online Constrained Model-based Reinforcement Learning. CoRR abs/2004.03499 (2020) - [i17]Shell Xu Hu, Pablo Garcia Moreno, Yang Xiao, Xi Shen, Guillaume Obozinski, Neil D. Lawrence, Andreas C. Damianou:
Empirical Bayes Transductive Meta-Learning with Synthetic Gradients. CoRR abs/2004.12696 (2020) - [i16]Francesco Tonolini, Pablo Garcia Moreno, Andreas C. Damianou, Roderick Murray-Smith:
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data. CoRR abs/2006.16938 (2020)
2010 – 2019
- 2019
- [c19]Sungsoo Ahn, Shell Xu Hu, Andreas C. Damianou, Neil D. Lawrence, Zhenwen Dai:
Variational Information Distillation for Knowledge Transfer. CVPR 2019: 9163-9171 - [c18]Sebastian Flennerhag, Pablo Garcia Moreno, Neil D. Lawrence, Andreas C. Damianou:
Transferring Knowledge across Learning Processes. ICLR 2019 - [i15]Kurt Cutajar, Mark Pullin, Andreas C. Damianou, Neil D. Lawrence, Javier González:
Deep Gaussian Processes for Multi-fidelity Modeling. CoRR abs/1903.07320 (2019) - [i14]Sungsoo Ahn, Shell Xu Hu, Andreas C. Damianou, Neil D. Lawrence, Zhenwen Dai:
Variational Information Distillation for Knowledge Transfer. CoRR abs/1904.05835 (2019) - [i13]Bharathan Balaji, Jordan Bell-Masterson, Enes Bilgin, Andreas C. Damianou, Pablo Moreno Garcia, Arpit Jain, Runfei Luo, Alvaro Maggiar, Balakrishnan Narayanaswamy, Chun Ye:
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems. CoRR abs/1911.10641 (2019) - 2018
- [j2]Clément Moulin-Frier, Tobias Fischer, Maxime Petit, Grégoire Pointeau, Jordi-Ysard Puigbo, Ugo Pattacini, Sock Ching Low, Daniel Camilleri, Phuong D. H. Nguyen, Matej Hoffmann, Hyung Jin Chang, Martina Zambelli, Anne-Laure Mealier, Andreas C. Damianou, Giorgio Metta, Tony J. Prescott, Yiannis Demiris, Peter Ford Dominey, Paul F. M. J. Verschure:
DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self. IEEE Trans. Cogn. Dev. Syst. 10(4): 1005-1022 (2018) - [c17]Jie Yang, Thomas Drake, Andreas C. Damianou, Yoelle Maarek:
Leveraging Crowdsourcing Data for Deep Active Learning An Application: Learning Intents in Alexa. WWW 2018: 23-32 - [i12]Jie Yang, Thomas Drake, Andreas C. Damianou, Yoelle Maarek:
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa. CoRR abs/1803.04223 (2018) - [i11]Vinayak Kumar, Vaibhav Singh, P. K. Srijith, Andreas C. Damianou:
Deep Gaussian Processes with Convolutional Kernels. CoRR abs/1806.01655 (2018) - [i10]Sebastian Flennerhag, Pablo Garcia Moreno, Neil D. Lawrence, Andreas C. Damianou:
Transferring Knowledge across Learning Processes. CoRR abs/1812.01054 (2018) - 2017
- [c16]Javier González, Zhenwen Dai, Andreas C. Damianou, Neil D. Lawrence:
Preferential Bayesian Optimization. ICML 2017: 1282-1291 - [c15]Ming Jin, Andreas C. Damianou, Pieter Abbeel, Costas J. Spanos:
Inverse Reinforcement Learning via Deep Gaussian Process. UAI 2017 - [c14]Benjamin van Niekerk, Andreas C. Damianou, Benjamin Rosman:
Online Constrained Model-based Reinforcement Learning. UAI 2017 - [i9]Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek:
Manifold Alignment Determination: finding correspondences across different data views. CoRR abs/1701.03449 (2017) - [i8]Clément Moulin-Frier, Tobias Fischer, Maxime Petit, Grégoire Pointeau, Jordi-Ysard Puigbo, Ugo Pattacini, Sock Ching Low, Daniel Camilleri, Phuong D. H. Nguyen, Matej Hoffmann, Hyung Jin Chang, Martina Zambelli, Anne-Laure Mealier, Andreas C. Damianou, Giorgio Metta, Tony J. Prescott, Yiannis Demiris, Peter Ford Dominey, Paul F. M. J. Verschure:
DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self. CoRR abs/1706.03661 (2017) - 2016
- [j1]Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence:
Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes. J. Mach. Learn. Res. 17: 42:1-42:62 (2016) - [c13]Yasemin Bekiroglu, Andreas C. Damianou, Renaud Detry, Johannes A. Stork, Danica Kragic, Carl Henrik Ek:
Probabilistic consolidation of grasp experience. ICRA 2016: 193-200 - [c12]Daniel Camilleri, Andreas C. Damianou, Harry Jackson, Neil D. Lawrence, Tony J. Prescott:
iCub Visual Memory Inspector: Visualising the iCub's Thoughts. Living Machines 2016: 48-57 - [c11]Uriel Martinez-Hernandez, Andreas C. Damianou, Daniel Camilleri, Luke W. Boorman, Neil D. Lawrence, Tony J. Prescott:
An integrated probabilistic framework for robot perception, learning and memory. ROBIO 2016: 1796-1801 - [c10]Daniel Camilleri, Luke W. Boorman, Uriel Martinez-Hernandez, Andreas C. Damianou, Tony J. Prescott:
A Bioinspired Approach to Vision. TAROS 2016: 40-52 - [c9]Zhenwen Dai, Andreas C. Damianou, Javier González, Neil D. Lawrence:
Variational Auto-encoded Deep Gaussian Processes. ICLR (Poster) 2016 - [c8]César Lincoln C. Mattos, Zhenwen Dai, Andreas C. Damianou, Jeremy Forth, Guilherme A. Barreto, Neil D. Lawrence:
Recurrent Gaussian Processes. ICLR (Poster) 2016 - [i7]Andreas C. Damianou, Neil D. Lawrence, Carl Henrik Ek:
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis. CoRR abs/1604.04939 (2016) - 2015
- [c7]Andreas C. Damianou, Carl Henrik Ek, Luke Boorman, Neil D. Lawrence, Tony J. Prescott:
A Top-Down Approach for a Synthetic Autobiographical Memory System. Living Machines 2015: 280-292 - [c6]Luke W. Boorman, Andreas C. Damianou, Uriel Martinez-Hernandez, Tony J. Prescott:
Extending a Hippocampal Model for Navigation Around a Maze Generated from Real-World Data. Living Machines 2015: 441-452 - [c5]Andreas C. Damianou, Neil D. Lawrence:
Semi-described and semi-supervised learning with Gaussian processes. UAI 2015: 228-237 - [i6]Andreas C. Damianou, Neil D. Lawrence:
Semi-described and semi-supervised learning with Gaussian processes. CoRR abs/1509.01168 (2015) - 2014
- [c4]Deepak Vasisht, Andreas C. Damianou, Manik Varma, Ashish Kapoor:
Active learning for sparse bayesian multilabel classification. KDD 2014: 472-481 - [i5]Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence:
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models. CoRR abs/1409.2287 (2014) - [i4]Zhenwen Dai, Andreas C. Damianou, James Hensman, Neil D. Lawrence:
Gaussian Process Models with Parallelization and GPU acceleration. CoRR abs/1410.4984 (2014) - 2013
- [c3]Andreas C. Damianou, Neil D. Lawrence:
Deep Gaussian Processes. AISTATS 2013: 207-215 - 2012
- [c2]Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence:
Manifold Relevance Determination. ICML 2012 - [i3]Andreas C. Damianou, Carl Henrik Ek, Michalis K. Titsias, Neil D. Lawrence:
Manifold Relevance Determination. CoRR abs/1206.4610 (2012) - [i2]Andreas C. Damianou, Neil D. Lawrence:
Deep Gaussian Processes. CoRR abs/1211.0358 (2012) - 2011
- [c1]Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence:
Variational Gaussian Process Dynamical Systems. NIPS 2011: 2510-2518 - [i1]Andreas C. Damianou, Michalis K. Titsias, Neil D. Lawrence:
Variational Gaussian Process Dynamical Systems. CoRR abs/1107.4985 (2011)
Coauthor Index
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